Predicting 30- to 120-day readmission risk among Medicare fee-for-service patients using nonmedical workers and mobile technology
Purpose
The purpose of the instant research is to establish how the use of technology can enhance the capacity of nonmedical staff to carry out obligations ordinarily limited to nursing staff. The main reason for replacement of nursing managers with nonmedical staff is cost cutting. Yet through inter alia patient readmissions, cost cutting measures can backfire and lead to higher costs (Ostrovsky, 2016).
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The research question therefore is, can the use of mobile phones based algorithm based apps to enhance communication and coordination between nursing and nonmedical staff handling patients reduce propensity for 30-120 day patient readmission.
The hypothesis for the research is that when patients are handled by nurses there is a lower readmission rate than when they are handled by nonmedical staff. Increased communication and coordination using technology between nonmedical staff handling patients and nurses can therefore reduce propensity for readmission.
Research Methods
The research method used is observatory not experimental. The researchers evaluate a program that has been put to use by Elder Services of Merrimack Valley (ESMV) (Ostrovsky, 2016). There is no logical control by the researchers hence the classification.
The data used was collected through the administrations systems at ESMV as well as the data collected by the apps being used in the new system. All data collection is computerized and managed by ESMV (Ostrovsky, 2016).
The data collected was purely limited to numbers and therefore fall under the ambit of quantitative data. It relates to the number of patients handled, the number of patients forwarded by the nonmedical staff to the nursing staff as well as the number of readmissions done. No categorical data was collected.
Among the potential weaknesses of the data collected was that it was purely based on question and answer system, conducted by non-medical staff who do not qualify to make secondary observations. This limits the accuracy of the data to both the honesty of the patients themselves and the accuracy of the information given by the patients.
The method of statistical analysis used within the group handled using the software was based on the Stata statistical software release 11. It compared the risk propensity for patients based on different characteristics of the patients. To compare admission rates between patients handled before the program began and after the program began, a between-subjects analysis of variance was done (ANOVA) (Ostrovsky, 2016).
This data analysis method had a material weakness in that it did not factor even unexpected eventualities such as patients who are taken suddenly ill despite having been ably handled during the visit or telephonic interaction with non-medical staff.
The key demographics used were twofold. First was the general demographic used which was that of poor elderly patients. Indeed this was the only age-group handled by ESMV, a humanitarian organization. This group was then evaluated according to gender and racial profile.
Key Findings
5,224 surveys were performed using the app out of which 1,202 referrals were made to nursing managers. This data was deemed to be significant as the number of patient handled by nonmedical staff was 23% of the total number of patients. The system could therefore allow for over 70% of the patients to be handled by medical staff. Of the patients referred 882 had no readmission within 30 days while 242 did have readmissions. From a broader perspective, the 30 day readmission rate for the 3 months prior to the program averaged at 17.6 % while that within the program averaged at 16.4% (Ostrovsky, 2016).
There was a very small difference between the number of patients readmitted before and after the introduction of the program. The main variance however was a distinct way of establishing which patients are to be managed by nonmedical staff only and which are to be referred to nursing managers who came at a higher cost for the organization. This capacity to categorize is the main difference between the two groups before and after the program commenced.
Limitations
The population based limitation in the instant study was that the entire sample population was of an elevated age which averaged at 73 years. Further, this population was relatively poor, based on the fact that the program was based on a free medical services humanitarian organization. The research findings are, therefore, not reliable and valid for used in the general population.
The main statistical analysis design used compared the numbers of patients before and after the program. The main advantage of this design is simplicity hence accuracy as being a direct comparison between two amounts, the propensity of error is diminished thus increasing reliability. However, in its simplicity lies the disadvantage of inability to factor secondary variables. The research compared data from a period where no intense oversight was undertaken with the period of the study when algorithms were used to calculate several variables. This exponentially reduced the validity of the research.
Being an observatory research, the instant study was limited in that the researcher could not set the parameters for the research and had only to rely on the systems put in place by the ESMV. The data and outcomes of the research relied on the competence and accuracy of the systems set down by ESMV for its validity and reliability. The researchers were reduced to playing the part of guests of ESMV and were never in actual control of the situation.
Major Conclusions
The main conclusions of the research was that the observations of non-medical staff can under the right circumstances and with the right tools be used to predict patient readmission propensity. However, the accuracy of these observations can be greatly enhanced if the nonmedical staffs are superintended by clinical staff. With specific regard to home visits and telephone contact with patients, this superintendence can be conducted through the use of algorithm based computer systems accessible through telephone apps. These findings are congruent to the findings reported in Kaushal et al, (2010) which relates to the combination of personal expertise with technology to improve performance. The study investigated the use of algorithm based technology to make the complex and sometimes sensitive decision of whether or not to admit patients. Using e-prescribing was seen to exponentially reduce the propensity of error in making this critical decision.
Technology is taking over in many areas of the medical profession, in most cases exponentially reducing the need for professional skill. With there being an increasing deficit of qualified nurses, ways and means of using nonmedical staff in official duties are being developed. This research contributes to the instant field in showing how technology can be used to enhance the capacity of nonmedical staff to effectively perform duties erstwhile reserved for qualified and licensed medical staff.
Further research is necessary to show how secondary factors such as computer proficiency and patient honesty more so for some demographics such as the very old or children affect the efficacy of algorithm based programs such as the one used in the instant study.
References
Ostrovsky, A., O’Conner, L., Marshall, O., Angelo, A., Barrett, K., Majeski, E., . . . Levy, J. (2016). Predicting 30- to 120-day readmission risk among Medicare fee-for-service patients using nonmedical workers and mobile technology. Perspectives in Health Information Management, Winter 2016, 1–20
Kaushal, R., Kern, L. M., Barrón, Y., Quaresimo, J., & Abramson, E. L. (2010). Electronic prescribing improves medication safety in community-based office practices. Journal of General Internal Medicine, 25 (6), 530–536.
